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nani_nsurp_finalTables.py
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nani_nsurp_finalTables.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Mar 1 12:06:12 2021
This script is used to process FinalTables for the hybridized metrics
that MPennino has utilized for his paper. Each of these are based on other
previously produced metrics with some simple table math to arrive at the final
metric.
@author: Rdebbout
"""
import zipfile
from pathlib import Path
import numpy as np
import pandas as pd
from stream_cat_config import FINAL_DIR, LENGTHS, OUT_DIR
def build_stats(tbl, stats):
if not stats:
for c in tbl.columns.tolist():
stats[c] = {"min": tbl[c].min(), "max": tbl[c].max()}
return stats
for col in tbl.columns.tolist():
if tbl[col].min() < stats[col]["min"]:
stats[col]["min"] = tbl[col].min()
if tbl[col].max() > stats[col]["max"]:
stats[col]["max"] = tbl[col].max()
return stats
LENGTH_ERROR_MESSAGE = (
"Table {} length vpu {} incorrect!!!!"
"...check Allocation and Accumulation results"
)
OUT_DIR = Path(OUT_DIR)
FINAL_DIR = Path(FINAL_DIR)
ctl = pd.read_csv("ControlTable_StreamCat.csv")
inputs = np.load("accum_npy/vpu_inputs.npy", allow_pickle=True).item()
states_lookup = Path("state_dict.npz")
states_dict = np.load(str(states_lookup), allow_pickle=True, encoding="latin1")[
"data"
].item()
STATES_DIR = FINAL_DIR.parents[0] / "States"
region_fn = "{}_Region{}.csv"
stats = dict()
for vpu in inputs:
out_file = FINAL_DIR / region_fn.format("Nsurp_NANI", vpu)
# Check if output tables exist before writing
if not out_file.exists():
tbl = pd.read_csv(FINAL_DIR / f"AgriculturalNitrogen_Region{vpu}.csv")
front_cols = [
title
for title in tbl.columns
for x in ["COMID", "AreaSqKm"]
if x in title and not "Up" in title
]
tbl["Fert06_kg_Cat"] = tbl.FertCat * tbl.CatAreaSqKm * 100
tbl["Fert06_kg_Ws"] = tbl.FertWs * tbl.WsAreaSqKm * 100
tbl["Manure06_kg_Cat"] = tbl.ManureCat * tbl.CatAreaSqKm * 100
tbl["Manure06_kg_Ws"] = tbl.ManureWs * tbl.WsAreaSqKm * 100
tbl["CBNF06_kg_Cat"] = tbl.CBNFCat * tbl.CatAreaSqKm * 100
tbl["CBNF06_kg_Ws"] = tbl.CBNFWs * tbl.WsAreaSqKm * 100
tbl["Livestock_N_Content_kg_Cat"] = tbl.Manure06_kg_Cat * 0.25 * 1.37
tbl["Livestock_N_Content_kg_Ws"] = tbl.Manure06_kg_Ws * 0.25 * 1.37
tbl["Livestock_N_Demand_kg_Cat"] = tbl.Manure06_kg_Cat * 1.37
tbl["Livestock_N_Demand_kg_Ws"] = tbl.Manure06_kg_Ws * 1.37
t = tbl[
[
"COMID",
"CatAreaSqKm",
"WsAreaSqKm",
"Fert06_kg_Cat",
"Fert06_kg_Ws",
"Manure06_kg_Cat",
"Manure06_kg_Ws",
"CBNF06_kg_Cat",
"CBNF06_kg_Ws",
"Livestock_N_Content_kg_Cat",
"Livestock_N_Content_kg_Ws",
"Livestock_N_Demand_kg_Cat",
"Livestock_N_Demand_kg_Ws",
]
]
urb_fert = pd.read_csv(OUT_DIR / f"N_Urb_Fert_{vpu}.csv")
urb_fert["UrbFert_kg_Cat"] = urb_fert.CatSum / 100_000
urb_fert["UrbFert_kg_Ws"] = urb_fert.WsSum / 100_000
t = pd.merge(
t, urb_fert[["COMID", "UrbFert_kg_Cat", "UrbFert_kg_Ws"]], on="COMID"
)
tdep = pd.read_csv(FINAL_DIR / f"TDEP_Region{vpu}.csv")
tdep["TNDep06_kg_Cat"] = tdep.N_TW2006Cat * tdep.CatAreaSqKm * 100
tdep["TNDep06_kg_Ws"] = tdep.N_TW2006Ws * tdep.WsAreaSqKm * 100
tdep["NOXI06_kg_Cat"] = tdep.NOXI_TW2006Cat * tdep.CatAreaSqKm * 100
tdep["NOXI06_kg_Ws"] = tdep.NOXI_TW2006Ws * tdep.WsAreaSqKm * 100
t = pd.merge(
t,
tdep[
[
"COMID",
"TNDep06_kg_Cat",
"TNDep06_kg_Ws",
"NOXI06_kg_Cat",
"NOXI06_kg_Ws",
]
],
on="COMID",
)
popden = pd.read_csv(FINAL_DIR / f"USCensus2010_Region{vpu}.csv")
popden["HumanWaste_kg_Cat"] = popden.PopDen2010Cat * popden.CatAreaSqKm * 4.7
popden["HumanWaste_kg_Ws"] = popden.PopDen2010Ws * popden.WsAreaSqKm * 4.7
popden["Human_N_Demand_kg_Cat"] = (
popden.PopDen2010Cat * popden.CatAreaSqKm * 6.21
)
popden["Human_N_Demand_kg_Ws"] = popden.PopDen2010Ws * popden.WsAreaSqKm * 6.21
t = pd.merge(
t,
popden[
[
"COMID",
"HumanWaste_kg_Cat",
"HumanWaste_kg_Ws",
"Human_N_Demand_kg_Cat",
"Human_N_Demand_kg_Ws",
]
],
on="COMID",
)
urb_rmv = pd.read_csv(OUT_DIR / f"N_rmv_{vpu}.csv")
urb_rmv["crop_N_rmv_kg_Cat"] = urb_rmv.CatSum / 10_000
urb_rmv["crop_N_rmv_kg_Ws"] = urb_rmv.WsSum / 10_000
t = pd.merge(
t,
urb_rmv[["COMID", "crop_N_rmv_kg_Cat", "crop_N_rmv_kg_Ws"]],
on="COMID",
)
t["NsurpCat"] = (
t.Fert06_kg_Cat
+ t.UrbFert_kg_Cat
+ t.TNDep06_kg_Cat
+ t.CBNF06_kg_Cat
+ t.Manure06_kg_Cat
+ t.HumanWaste_kg_Cat
) - t.crop_N_rmv_kg_Cat
t["NsurpWs"] = (
t.Fert06_kg_Ws
+ t.UrbFert_kg_Ws
+ t.TNDep06_kg_Ws
+ t.CBNF06_kg_Ws
+ t.Manure06_kg_Ws
+ t.HumanWaste_kg_Ws
) - t.crop_N_rmv_kg_Ws
t["NANICat"] = (
t.Fert06_kg_Cat
+ t.UrbFert_kg_Cat
+ t.CBNF06_kg_Cat
+ t.NOXI06_kg_Cat
+ t.Human_N_Demand_kg_Cat
+ t.Livestock_N_Demand_kg_Cat
- t.crop_N_rmv_kg_Cat
- t.Livestock_N_Content_kg_Cat
)
t["NANIWs"] = (
t.Fert06_kg_Ws
+ t.UrbFert_kg_Ws
+ t.CBNF06_kg_Ws
+ t.NOXI06_kg_Ws
+ t.Human_N_Demand_kg_Ws
+ t.Livestock_N_Demand_kg_Ws
- t.crop_N_rmv_kg_Ws
- t.Livestock_N_Content_kg_Ws
)
final = t[front_cols + ["NsurpCat", "NsurpWs", "NANICat", "NANIWs"]]
final = final.set_index("COMID")
stats = build_stats(final, stats)
final.fillna("NA", inplace=True)
if not LENGTHS[vpu] == len(final):
print(LENGTH_ERROR_MESSAGE.format(table, vpu))
final.to_csv(out_file)
# ZIP up every region as we write them out
zip_name = out_file.name.replace("csv", "zip")
zf = zipfile.ZipFile(str(FINAL_DIR / "zips" / zip_name), mode="w")
zf.write(str(out_file), out_file.name, compress_type=zipfile.ZIP_DEFLATED)
zf.close()
# Make the state tables
for state in states_dict:
print(state)
state_tbl = pd.DataFrame()
keepers = states_dict[state]["COMIDs"]
state_file = STATES_DIR / fn.format(table, state)
for vpu in states_dict[state]["VPUs"]:
vpu_tbl = pd.read_csv(FINAL_DIR / region_fn.format(table, vpu))
vpu_tbl.query("COMID in @keepers", inplace=True)
state_tbl = state_tbl.append(vpu_tbl)
state_tbl.to_csv(state_file, index=False)
# ZIP up every state as we write them out
zip_name = state_file.name.replace("csv", "zip")
zf = zipfile.ZipFile(str(STATES_DIR / "zips" / zip_name), mode="w")
zf.write(str(state_file), state_file.name, compress_type=zipfile.ZIP_DEFLATED)
zf.close()
for stat in stats:
print(stat + " " + str(stats[stat]))
print("All Done.....")